As interest in running continues growing, runners are constantly seeking ways to prevent common aches and injuries. The injury rate among runners is exceptionally high compared to other popular fitness activities, with estimates reporting that around 50% of runners are injured each year. Overstriding has been shown to increase stress on the body, as overstriding leads to a straighter knee, a stiffer leg, and a more aggressive heel/foot strike, which significantly increases peak ground reaction forces and reduces the knee muscles' ability to absorb shock. The shock is then transferred to the knee menisci, knee joint and on to the hip and back joints. It also causes a greater amount of braking force that pushes the body backwards, thus causing the runner to lose speed and have to reaccelerate. Research has shown that maintaining proper running form is extremely beneficial both in reducing the chance of injury and maximizing energy efficiency.
Gait analysis has been shown to be helpful in retraining gait when overstriding is occurring. However, current technology requires training center equipment and a human analyst to perform the gait analysis within lab training centers, meaning runners are running in constructed, artificial settings. This can lead to errors in the gait analysis, since running on treadmills may differ from running in natural settings. Additionally, trainers are required to provide instruction and feedback. Because of this reliance on external equipment and human analysis, runners are not able to independently receive accurate feedback on their running form outside of the training sessions at the training centers. Examples of existing gait retraining techniques include: visual training, where a runner runs on a treadmill using mirrors or lasers for feedback; verbal feedback, where a coach or trainer monitors a runner's gait and provides instruction; and post-activity feedback, where runners capture a run with high speed cameras or motion capture systems, and analyze their form afterwards.
Other existing approaches utilize various sensor arrangements to monitor a person's gait for overstriding, without the need for visual observation by a second person or being confined to laboratory settings. However, these systems are complex, often requiring a multitude sensors on both legs in order to gather the large amounts of information necessary to detect overstriding via their respective approaches. For example, many such approaches require a full picture of how the entire leg is moving in order to determine whether the person is overstriding, and thus utilize a multitude of sensors attached to several portions of the person's leg, such as the hip, thigh, knee, shank, ankle, and/or foot. Additionally or alternatively, many such approaches require information concerning the motions of both legs of the person, and analyzing that information together, in order to determine if one or both of the person's legs are overstriding. This large amount of information must be stored and processed, and therefore require larger processors, memory capacity, and battery power. As a result, these systems tend to require complex calibration techniques to sync up the various sensors, and suffer from poor reliability when one or more of the sensors slip or drift on the person's body during locomotion. Further, these systems tend to be heavy and bulky, which can affect the person's locomotive performance and make the system more noticeable when worn or carried. Still further, the complexity of these systems can make them rather difficult and time consuming to don and take off.
Therefore, there is a need for a simple, lightweight, portable and transparent monitoring system for detecting gait overstriding.